Deep Transfer Learning Strategy in Intelligent Fault Diagnosis of Gas Turbines Based on Thekoopman Operator

The gas turbine engine is a predominant prime mover in the transport and energy sectors, and ensuring its reliable operationholds paramount significance. While intelligent fault diagnosis (FD) approaches have seen successful advancements within the gasturbine FD landscape, many existing methods operate under the assumption of identical health states during both data collectionand the FD process. Moreover, most previous studies have overlooked the diagnosis of both sensors and actuators. Another criticalchallenge lies in isolating simultaneous and multiple faults and providing compromising FD performance, especially in the face ofcontinued system performance degradation. Aiming at these problems, this study develops a novel unsupervised data-driven FDstrategy based on leveraging the potential of transfer learning and the Koopman operator. A deep neural network-based transferlearning framework is proposed for realizing a precise adaptive linear model called the deep transfer linear (DTL) model enablingreliable prediction of the system’s behavior in various situations and designing structured fault residuals. To this end, a deep neuralnetwork framework is used to obtain a precise Koopman model realization using the richly collected data in the source domain.Subsequently, the realized model is fine-tuned for the target domains associated with the degraded system, mitigating the adversee ects of domain shift and addressing the rich data scarcity problem in the target domain. In addition, the dedicated and generalizedresidual sets are designed and generated employing the geometric approach for fault isolation and a decision-making analysis isdeveloped to diagnose simultaneous faults. The reliability of the proposed strategy is demonstrated through various experimentsin the presence of noise and performance degradation, and a comparative performance analysis is conducted between the proposedstrategy and another data-driven method showcasing the superiority of the proposed approach.